Generating complete 3D scenes from a single image requires inferring globally consistent geometry, object relationships, and environmental context from inherently ambiguous visual evidence. Despite recent progress in joint layout-and-mesh generation, existing methods often rely on holistic or weakly decomposed pipelines that entangle many factors at once and demand extensive scene-level supervision, limiting their generalization to complex real-world environments. We propose a multi-agent orchestration framework that decomposes single-image 3D scene generation into three structured stages: scene initialization, environment construction, and multi-agent refinement. The initialization stage extracts image-derived object masks, builds object-level 3D representations, and predicts an initial spatial layout to form a coarse 3D scene. The environment-construction stage then leverages this initialization together with point-map geometry to build an environmental scaffold of supporting surfaces, room boundaries, materials, and illumination. Finally, in the refinement stage, a planner agent identifies structural and visual inconsistencies, applies simple corrections directly, and dispatches specialist agents for complex localized revisions that are reintegrated into the global scene. To provide reliable structural initialization while reducing reliance on scene-level annotations, we further introduce a geometry-aware layout predictor supervised by sparse geometric priors derived from point maps. Unlike fully supervised layout generators, the predictor can be trained from segmentation-level data and generalizes robustly to diverse real-world scenes. Extensive experiments on benchmark datasets show that our method consistently outperforms prior approaches in geometric accuracy, spatial consistency, and perceptual realism.

翻译：从单张图像生成完整的三维场景，需要从本质上模糊的视觉证据中推断出全局一致的几何结构、物体关系及环境上下文。尽管近期在联合布局与网格生成方面取得了进展，现有方法通常依赖整体式或弱分解的流水线，这一方式同时耦合诸多因素且需要大量场景级监督，限制了其在复杂真实世界环境中的泛化能力。我们提出一种多智能体编排框架，将单图像三维场景生成分解为三个结构化阶段：场景初始化、环境构建与多智能体细化。初始化阶段提取图像衍生的物体掩码，构建物体级三维表征，并预测初始空间布局以形成粗略三维场景。环境构建阶段则利用该初始化信息及点图几何，构建支撑面、房间边界、材质与光照的环境骨架。最后在细化阶段，规划智能体识别结构性与视觉不一致性，直接执行简单修正，并派发专业智能体处理需重新整合至全局场景的复杂局部修正。为在减少场景级标注依赖的同时提供可靠的结构初始化，我们进一步引入一种几何感知布局预测器，该预测器由点图导出的稀疏几何先验监督。与全监督布局生成器不同，该预测器可基于分割级数据进行训练，并能稳健泛化至多样化的真实场景。在基准数据集上的大量实验表明，我们的方法在几何精度、空间一致性与感知真实感方面持续优于现有方法。